Artificial neural networks as prediction tools in the critically ill

Critical Care

Volumn 9, Issue 2, 2005, Pages 153-154

  Clermont, Gilles ^a  

^a UNIVERSITY OF PITTSBURGH SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ARTICLE; ARTIFICIAL NEURAL NETWORK; CRITICAL ILLNESS; EMERGENCY WARD; HUMAN; INTERMETHOD COMPARISON; LOGISTIC REGRESSION ANALYSIS; MORTALITY; MULTIPLE REGRESSION; OUTCOMES RESEARCH; PREDICTION; PRIORITY JOURNAL; QUANTITATIVE ANALYSIS; RELIABILITY; SEPSIS; SURVIVAL RATE; VALIDATION PROCESS;

COHORT STUDIES; CRITICAL ILLNESS; HOSPITAL MORTALITY; HUMANS; LOGISTIC MODELS; NEURAL NETWORKS (COMPUTER); PROGNOSIS; RISK FACTORS; SEPSIS;

EID: 15844376479     PISSN: 13648535     EISSN: None     Source Type: Journal    
DOI: 10.1186/cc3507     Document Type: Article

References (15)

1. Jaimes F, Farbiarz J, Alvarez D, Martinez C: Comparison between logistic regression and neural networks to predict death in patients with suspected sepsis in the emergency room. Crit Care 2005, 9:R150-R156.
2. Lemeshow S, Klar J, Teres D: Outcome prediction for individual intensive care patients: useful, misused, or abused? Intensive Care Med 1995, 21:770-776.
3. Clermont G, Angus DC: Severity scoring systems in the modern intensive care unit. Ann Acad Med Singapore 1998, 27: 97-403.
4. Lemeshow S, Teres D, Avrunin JS, Pastides H: Predicting the outcome of intensive care unit patients. J Am Stat Assoc 1988, 83:348-356.
5. Schuster DP: Predicting outcome after ICU admission. The art and science of assessing risk. Chest 1992, 102: 861-1870.
6. Lemeshow S, Hosmer DW Jr: A review of goodness of fit statistics for use in the development of logistic regression models. Am J Epidemiol 1982, 115:92-106.
7. Buchman TG, Kubos KL, Seidler AJ, Siegforth MJ: A comparison of statistical and connectionist models for the prediction of chronicity in a surgical intensive care unit. Crit Care Med 1994, 22:750-762.
8. Doyle HR, Dvorchik I, Mitchell S, Marino IR, Ebert FH, McMichael J, Fung JJ: Predicting outcomes after liver transplantation. A connectionist approach. Ann Surg 1994, 219:408-415.
9. Doig GS, Inman KJ, Sibbald WJ, Martin CM, Robertson JM: Modeling mortality in the intensive care unit: comparing the performance of a back-propagation, associative-learning neural network with multivariate logistic regression. Proc Annu Symp Comput Appl Med Care 1993, 361-365.
10. Hotchkiss JR, Broccard AF, Crooke PS: Artificial neural network prediction of ventilator-induced lung edema formation. Crit Care Med 2003, 31:2250.
11. Clermont G, Angus D, DiRusso S, Griffin M, Linde-Zwirble W: Predicting hospital mortality for patients in the intensive care unit: a comparison of artificial neural networks with logistic regression models. Crit Care Med 2001, 29:291-296.
12. Randolph AG, Gordon HG, Calvin JE, Doig G, Richardson WS: Understanding articles describing clinical prediction tools. Crit Care Med 1998, 26:1603-1612.
13. de Laurentiis M, Ravdin PM: A technique for using neural network analysis to perform survival analysis of censored data. Cancer Lett 1994, 77:127-138.
14. Wyatt J: Nervous about artificial neural networks? Lancet 1995, 346:1175-1177.
15. Schwarzer G, Vach W, Schumacher M: On the misuses of artificial neural networks for prognostic and diagnostic classification in oncology. Stat Med 2000, 19:541-561.